Setup is as straightforward as popping one in a socket, pairing it with your network, and then using it to switch on and off any appliance attached to it. They might look like an ordinary adaptor or extension lead, but they can do so much more, connecting to your wifi and letting you manage your gadgets from an app on your phone. If you're not home, have lamps go on and off to make it look like you are or you can double-check you definitely switched off your hair straighteners – we've all been there.Īlong with smart bulbs and video doorbells, these devices are among the simplest and most affordable ways to make your home more connected, enabling you to control your appliances remotely and set them to a schedule from your phone. Need a cuppa right away? You can get the kettle boiling before you reach the front door. When you think of the possibilities, it's easy to see how turning plugs on and off could be useful. The best smart plugs are a quick way to make your home more intelligent, turning any simple appliance into one that connects to your wifi. The results show that kNN and SVR show lower error.We updated this roundup in March 2023 to guarantee that our best smart plug picks were in stock and reflected up-to-date prices. This has been pursued by: first, we have solely relied on past values of solar power data (rather than external data), hence lowering the volume of input data second, the investigated algorithms are capable of generating predictions in less than a second. Since predictions are made on every minute for one minute ahead values, the designed system has to be rapidly responsive. In general, both datasets generate comparable prediction error.įorecasting solar power generation with application on real-time control of energy system has also been investigated. On the other hand, station records provide relatively slower prediction while respecting the customer privacy. We found that charging records provide relatively faster prediction while putting customer privacy at jeopardy. Based on non-parametric statistical tests, suitable (or unsuitable) imputation methods for each prediction algorithm are recommended.įorecasting of the Electric Vehicle (EV) charging load can be done based on two different datasets: data from the customer profile (charging record) and data from outlet measurements (station record). Six different imputation methods have been applied to compensate for missing values in EV charging data. ![]() Using our dataset, TWDP NN decreased the processing time by a third.Īs missing data is a significant concern in real world data, the effect of missing values on the prediction quality has been investigated. One of the objectives when predicting the EV charging load is speed of prediction since it is intended to be used in a real time application (smartphone application for EV customers). In the process of applying these algorithms on the Electric Vehicle (EV) charging load prediction problem, two new prediction algorithms have been proposed, namely Modified Pattern Sequence Forecasting (MPSF) and Time Weighted Dot Product Nearest Neighbor (TWDP NN). ![]() We have used real world data from the UCLA campus solar PV panels and parking lots. In view of the success of machine learning based prediction algorithms in the recent years, in this study, we have employed a selection of these algorithms on some time series prediction problems in the context of smart grid.
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